Overview

Dataset statistics

Number of variables32
Number of observations5500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory288.0 B

Variable types

Categorical12
Numeric20

Alerts

device_fraud_count has constant value "0"Constant
foreign_request is highly imbalanced (77.7%)Imbalance
source is highly imbalanced (93.9%)Imbalance
device_distinct_emails_8w is highly imbalanced (79.2%)Imbalance
name_email_similarity has unique valuesUnique
days_since_request has unique valuesUnique
intended_balcon_amount has unique valuesUnique
velocity_6h has unique valuesUnique
velocity_24h has unique valuesUnique
velocity_4w has unique valuesUnique
bank_branch_count_8w has 1021 (18.6%) zerosZeros
employment_status has 4220 (76.7%) zerosZeros
housing_status has 1957 (35.6%) zerosZeros
month has 734 (13.3%) zerosZeros

Reproduction

Analysis started2023-07-17 08:05:48.375600
Analysis finished2023-07-17 08:06:34.142479
Duration45.77 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

fraud_bool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
2777 
1
2723 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2777
50.5%
1 2723
49.5%

Length

2023-07-17T14:36:34.207263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:34.313359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2777
50.5%
1 2723
49.5%

Most occurring characters

ValueCountFrequency (%)
0 2777
50.5%
1 2723
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2777
50.5%
1 2723
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2777
50.5%
1 2723
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2777
50.5%
1 2723
49.5%

income
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61781818
Minimum0.1
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:34.391488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.4
median0.7
Q30.9
95-th percentile0.9
Maximum0.9
Range0.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.28819087
Coefficient of variation (CV)0.4664655
Kurtosis-1.0127551
Mean0.61781818
Median Absolute Deviation (MAD)0.2
Skewness-0.66245063
Sum3398
Variance0.083053977
MonotonicityNot monotonic
2023-07-17T14:36:34.491686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.9 1757
31.9%
0.8 809
14.7%
0.1 708
12.9%
0.6 570
 
10.4%
0.7 507
 
9.2%
0.4 385
 
7.0%
0.2 306
 
5.6%
0.5 246
 
4.5%
0.3 212
 
3.9%
ValueCountFrequency (%)
0.1 708
12.9%
0.2 306
 
5.6%
0.3 212
 
3.9%
0.4 385
 
7.0%
0.5 246
 
4.5%
0.6 570
 
10.4%
0.7 507
 
9.2%
0.8 809
14.7%
0.9 1757
31.9%
ValueCountFrequency (%)
0.9 1757
31.9%
0.8 809
14.7%
0.7 507
 
9.2%
0.6 570
 
10.4%
0.5 246
 
4.5%
0.4 385
 
7.0%
0.3 212
 
3.9%
0.2 306
 
5.6%
0.1 708
12.9%

name_email_similarity
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43993356
Minimum0.00041230123
Maximum0.99990304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:34.618936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.00041230123
5-th percentile0.049570759
Q10.16306603
median0.40105978
Q30.71988657
95-th percentile0.90917754
Maximum0.99990304
Range0.99949074
Interquartile range (IQR)0.55682054

Descriptive statistics

Standard deviation0.29724521
Coefficient of variation (CV)0.67565932
Kurtosis-1.3021793
Mean0.43993356
Median Absolute Deviation (MAD)0.26223537
Skewness0.27221965
Sum2419.6346
Variance0.088354715
MonotonicityNot monotonic
2023-07-17T14:36:34.753638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7792448406 1
 
< 0.1%
0.5512529286 1
 
< 0.1%
0.1353800828 1
 
< 0.1%
0.8178033991 1
 
< 0.1%
0.7755687696 1
 
< 0.1%
0.1513656895 1
 
< 0.1%
0.05772770305 1
 
< 0.1%
0.3237178092 1
 
< 0.1%
0.5726008577 1
 
< 0.1%
0.887854906 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
0.0004123012334 1
< 0.1%
0.0004440927114 1
< 0.1%
0.0007856695796 1
< 0.1%
0.001000799831 1
< 0.1%
0.001407635748 1
< 0.1%
0.00167055161 1
< 0.1%
0.001807455147 1
< 0.1%
0.001939988627 1
< 0.1%
0.001963046779 1
< 0.1%
0.002537553055 1
< 0.1%
ValueCountFrequency (%)
0.9999030413 1
< 0.1%
0.9998052127 1
< 0.1%
0.9997921896 1
< 0.1%
0.9997304468 1
< 0.1%
0.9994507157 1
< 0.1%
0.9993550212 1
< 0.1%
0.9993363703 1
< 0.1%
0.9993278189 1
< 0.1%
0.9992479729 1
< 0.1%
0.9992063694 1
< 0.1%

prev_address_months_count
Real number (ℝ)

Distinct202
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.304727
Minimum-1
Maximum361
Zeros0
Zeros (%)0.0%
Negative4463
Negative (%)81.1%
Memory size85.9 KiB
2023-07-17T14:36:34.876271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile81.05
Maximum361
Range362
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38.824015
Coefficient of variation (CV)3.4343168
Kurtosis29.331354
Mean11.304727
Median Absolute Deviation (MAD)0
Skewness4.9377802
Sum62176
Variance1507.3041
MonotonicityNot monotonic
2023-07-17T14:36:34.995458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 4463
81.1%
11 45
 
0.8%
30 44
 
0.8%
10 41
 
0.7%
31 37
 
0.7%
29 37
 
0.7%
28 33
 
0.6%
27 30
 
0.5%
12 29
 
0.5%
13 29
 
0.5%
Other values (192) 712
 
12.9%
ValueCountFrequency (%)
-1 4463
81.1%
8 6
 
0.1%
9 19
 
0.3%
10 41
 
0.7%
11 45
 
0.8%
12 29
 
0.5%
13 29
 
0.5%
14 3
 
0.1%
16 2
 
< 0.1%
17 2
 
< 0.1%
ValueCountFrequency (%)
361 1
< 0.1%
360 1
< 0.1%
346 1
< 0.1%
344 1
< 0.1%
341 1
< 0.1%
334 1
< 0.1%
332 1
< 0.1%
326 1
< 0.1%
322 1
< 0.1%
321 2
< 0.1%
Distinct377
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.846909
Minimum-1
Maximum402
Zeros27
Zeros (%)0.5%
Negative14
Negative (%)0.3%
Memory size85.9 KiB
2023-07-17T14:36:35.124264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile5
Q134
median73
Q3145
95-th percentile287.05
Maximum402
Range403
Interquartile range (IQR)111

Descriptive statistics

Standard deviation88.254537
Coefficient of variation (CV)0.88389854
Kurtosis1.0612493
Mean99.846909
Median Absolute Deviation (MAD)50
Skewness1.2279669
Sum549158
Variance7788.8633
MonotonicityNot monotonic
2023-07-17T14:36:35.251135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 72
 
1.3%
7 71
 
1.3%
5 67
 
1.2%
3 63
 
1.1%
11 60
 
1.1%
8 57
 
1.0%
2 53
 
1.0%
36 52
 
0.9%
40 52
 
0.9%
14 49
 
0.9%
Other values (367) 4904
89.2%
ValueCountFrequency (%)
-1 14
 
0.3%
0 27
 
0.5%
1 30
0.5%
2 53
1.0%
3 63
1.1%
4 48
0.9%
5 67
1.2%
6 72
1.3%
7 71
1.3%
8 57
1.0%
ValueCountFrequency (%)
402 1
 
< 0.1%
390 1
 
< 0.1%
389 1
 
< 0.1%
388 1
 
< 0.1%
387 1
 
< 0.1%
385 1
 
< 0.1%
384 1
 
< 0.1%
383 1
 
< 0.1%
382 4
0.1%
381 4
0.1%

customer_age
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.12
Minimum10
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:35.364358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q130
median40
Q350
95-th percentile60
Maximum80
Range70
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.017123
Coefficient of variation (CV)0.35067681
Kurtosis-0.33841386
Mean37.12
Median Absolute Deviation (MAD)10
Skewness0.34821242
Sum204160
Variance169.4455
MonotonicityNot monotonic
2023-07-17T14:36:35.464476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
30 1522
27.7%
40 1351
24.6%
50 1076
19.6%
20 1000
18.2%
60 386
 
7.0%
70 73
 
1.3%
10 71
 
1.3%
80 21
 
0.4%
ValueCountFrequency (%)
10 71
 
1.3%
20 1000
18.2%
30 1522
27.7%
40 1351
24.6%
50 1076
19.6%
60 386
 
7.0%
70 73
 
1.3%
80 21
 
0.4%
ValueCountFrequency (%)
80 21
 
0.4%
70 73
 
1.3%
60 386
 
7.0%
50 1076
19.6%
40 1351
24.6%
30 1522
27.7%
20 1000
18.2%
10 71
 
1.3%

days_since_request
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0974769
Minimum1.0131141 × 10-6
Maximum75.542228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:35.583748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0131141 × 10-6
5-th percentile0.0012942457
Q10.0067664271
median0.014260019
Q30.025296693
95-th percentile5.7720435
Maximum75.542228
Range75.542227
Interquartile range (IQR)0.018530265

Descriptive statistics

Standard deviation5.8317451
Coefficient of variation (CV)5.3137746
Kurtosis99.451938
Mean1.0974769
Median Absolute Deviation (MAD)0.0085243534
Skewness9.1470888
Sum6036.1232
Variance34.009251
MonotonicityNot monotonic
2023-07-17T14:36:35.701287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02110889945 1
 
< 0.1%
0.01117185114 1
 
< 0.1%
0.0006074459183 1
 
< 0.1%
0.009223247485 1
 
< 0.1%
0.009166839964 1
 
< 0.1%
0.01534107892 1
 
< 0.1%
0.005720793004 1
 
< 0.1%
0.005966488637 1
 
< 0.1%
0.02035226683 1
 
< 0.1%
0.02026578312 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
1.013114141 × 10-61
< 0.1%
3.764140761 × 10-61
< 0.1%
8.48461672 × 10-61
< 0.1%
8.709347877 × 10-61
< 0.1%
2.156641541 × 10-51
< 0.1%
2.601894185 × 10-51
< 0.1%
3.151253745 × 10-51
< 0.1%
3.510499228 × 10-51
< 0.1%
3.865886698 × 10-51
< 0.1%
3.941874931 × 10-51
< 0.1%
ValueCountFrequency (%)
75.54222839 1
< 0.1%
74.86882796 1
< 0.1%
74.86462468 1
< 0.1%
73.83174607 1
< 0.1%
72.99068764 1
< 0.1%
72.24382139 1
< 0.1%
72.15778385 1
< 0.1%
72.10941337 1
< 0.1%
71.78505923 1
< 0.1%
71.3739354 1
< 0.1%

intended_balcon_amount
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5908617
Minimum-6.3271116
Maximum110.94279
Zeros0
Zeros (%)0.0%
Negative4443
Negative (%)80.8%
Memory size85.9 KiB
2023-07-17T14:36:35.847195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.3271116
5-th percentile-1.5857968
Q1-1.2002076
median-0.87143294
Q3-0.42305433
95-th percentile49.721074
Maximum110.94279
Range117.2699
Interquartile range (IQR)0.7771533

Descriptive statistics

Standard deviation18.935875
Coefficient of variation (CV)2.87305
Kurtosis9.9068424
Mean6.5908617
Median Absolute Deviation (MAD)0.37200566
Skewness3.0175571
Sum36249.739
Variance358.56736
MonotonicityNot monotonic
2023-07-17T14:36:35.977703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.253772477 1
 
< 0.1%
-0.8325816232 1
 
< 0.1%
-0.9153381607 1
 
< 0.1%
20.41620595 1
 
< 0.1%
-0.7767550211 1
 
< 0.1%
-0.6535074651 1
 
< 0.1%
-1.188447238 1
 
< 0.1%
-0.4314345668 1
 
< 0.1%
-0.8663288789 1
 
< 0.1%
-0.3363496272 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
-6.327111566 1
< 0.1%
-4.955735289 1
< 0.1%
-4.772975962 1
< 0.1%
-4.543375583 1
< 0.1%
-4.244589691 1
< 0.1%
-3.607667155 1
< 0.1%
-3.455400025 1
< 0.1%
-3.283485615 1
< 0.1%
-2.860092751 1
< 0.1%
-2.633324413 1
< 0.1%
ValueCountFrequency (%)
110.9427907 1
< 0.1%
109.1411107 1
< 0.1%
109.122426 1
< 0.1%
107.8971166 1
< 0.1%
107.8820079 1
< 0.1%
106.5369373 1
< 0.1%
106.2991256 1
< 0.1%
105.9925994 1
< 0.1%
105.7679449 1
< 0.1%
105.6223722 1
< 0.1%

payment_type
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
1
2098 
2
1682 
0
1094 
3
625 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3
2nd row3
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2098
38.1%
2 1682
30.6%
0 1094
19.9%
3 625
 
11.4%
4 1
 
< 0.1%

Length

2023-07-17T14:36:36.099199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:36.211389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 2098
38.1%
2 1682
30.6%
0 1094
19.9%
3 625
 
11.4%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 2098
38.1%
2 1682
30.6%
0 1094
19.9%
3 625
 
11.4%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2098
38.1%
2 1682
30.6%
0 1094
19.9%
3 625
 
11.4%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2098
38.1%
2 1682
30.6%
0 1094
19.9%
3 625
 
11.4%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2098
38.1%
2 1682
30.6%
0 1094
19.9%
3 625
 
11.4%
4 1
 
< 0.1%

zip_count_4w
Real number (ℝ)

Distinct2559
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1596.2698
Minimum19
Maximum6336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:36.338665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile504.9
Q1910
median1303.5
Q31990.25
95-th percentile3675
Maximum6336
Range6317
Interquartile range (IQR)1080.25

Descriptive statistics

Standard deviation994.58384
Coefficient of variation (CV)0.6230675
Kurtosis2.0071388
Mean1596.2698
Median Absolute Deviation (MAD)478.5
Skewness1.4122368
Sum8779484
Variance989197.02
MonotonicityNot monotonic
2023-07-17T14:36:36.468068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
874 10
 
0.2%
1180 9
 
0.2%
1059 9
 
0.2%
901 9
 
0.2%
823 9
 
0.2%
888 9
 
0.2%
1064 9
 
0.2%
758 9
 
0.2%
675 8
 
0.1%
1113 8
 
0.1%
Other values (2549) 5411
98.4%
ValueCountFrequency (%)
19 1
< 0.1%
39 1
< 0.1%
43 1
< 0.1%
56 1
< 0.1%
64 1
< 0.1%
85 1
< 0.1%
96 1
< 0.1%
99 1
< 0.1%
104 1
< 0.1%
106 1
< 0.1%
ValueCountFrequency (%)
6336 1
< 0.1%
6262 1
< 0.1%
6135 1
< 0.1%
6037 1
< 0.1%
5857 1
< 0.1%
5803 1
< 0.1%
5763 1
< 0.1%
5755 1
< 0.1%
5749 1
< 0.1%
5717 1
< 0.1%

velocity_6h
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5459.956
Minimum54.188813
Maximum15901.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:36.916492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum54.188813
5-th percentile1204.4584
Q13175.2061
median5143.9913
Q37472.5023
95-th percentile10754.113
Maximum15901.17
Range15846.981
Interquartile range (IQR)4297.2961

Descriptive statistics

Standard deviation2993.4015
Coefficient of variation (CV)0.54824646
Kurtosis0.11488676
Mean5459.956
Median Absolute Deviation (MAD)2107.1733
Skewness0.61124667
Sum30029758
Variance8960452.8
MonotonicityNot monotonic
2023-07-17T14:36:37.055236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1312.045321 1
 
< 0.1%
14504.41637 1
 
< 0.1%
5182.636583 1
 
< 0.1%
1241.708703 1
 
< 0.1%
3019.412096 1
 
< 0.1%
5599.388883 1
 
< 0.1%
4937.943642 1
 
< 0.1%
4109.21276 1
 
< 0.1%
9206.178244 1
 
< 0.1%
13296.93897 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
54.18881317 1
< 0.1%
91.04217403 1
< 0.1%
231.9447747 1
< 0.1%
232.0702994 1
< 0.1%
283.5523572 1
< 0.1%
312.1043517 1
< 0.1%
321.204449 1
< 0.1%
337.2401055 1
< 0.1%
345.4902775 1
< 0.1%
355.244383 1
< 0.1%
ValueCountFrequency (%)
15901.16979 1
< 0.1%
15827.87064 1
< 0.1%
15798.41077 1
< 0.1%
15587.95438 1
< 0.1%
15514.56456 1
< 0.1%
15500.55085 1
< 0.1%
15453.23959 1
< 0.1%
15449.3707 1
< 0.1%
15446.31095 1
< 0.1%
15427.85188 1
< 0.1%

velocity_24h
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4692.6715
Minimum1501.3342
Maximum9359.9994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:37.186660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1501.3342
5-th percentile2542.9455
Q13498.7048
median4711.5902
Q35662.6667
95-th percentile7241.5974
Maximum9359.9994
Range7858.6651
Interquartile range (IQR)2163.9619

Descriptive statistics

Standard deviation1452.5167
Coefficient of variation (CV)0.30952873
Kurtosis-0.3645399
Mean4692.6715
Median Absolute Deviation (MAD)1067.8065
Skewness0.31421097
Sum25809694
Variance2109804.6
MonotonicityNot monotonic
2023-07-17T14:36:37.304883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2941.556881 1
 
< 0.1%
6999.810724 1
 
< 0.1%
2882.417564 1
 
< 0.1%
5178.522788 1
 
< 0.1%
3205.782614 1
 
< 0.1%
3839.773469 1
 
< 0.1%
5357.490414 1
 
< 0.1%
4096.711731 1
 
< 0.1%
5921.416961 1
 
< 0.1%
5584.842747 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
1501.334249 1
< 0.1%
1562.100494 1
< 0.1%
1609.577395 1
< 0.1%
1654.461645 1
< 0.1%
1654.913934 1
< 0.1%
1674.902146 1
< 0.1%
1679.280573 1
< 0.1%
1694.875261 1
< 0.1%
1712.666131 1
< 0.1%
1715.497236 1
< 0.1%
ValueCountFrequency (%)
9359.999387 1
< 0.1%
9355.276966 1
< 0.1%
9159.457608 1
< 0.1%
9137.144861 1
< 0.1%
9123.528092 1
< 0.1%
9112.394133 1
< 0.1%
9102.628855 1
< 0.1%
9089.964448 1
< 0.1%
9055.509978 1
< 0.1%
9046.645756 1
< 0.1%

velocity_4w
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4819.6322
Minimum2980.7328
Maximum6894.8177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:37.437121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2980.7328
5-th percentile3106.4542
Q14233.699
median4884.0574
Q35482.0771
95-th percentile6573.713
Maximum6894.8177
Range3914.0848
Interquartile range (IQR)1248.378

Descriptive statistics

Standard deviation951.85507
Coefficient of variation (CV)0.19749538
Kurtosis-0.48681282
Mean4819.6322
Median Absolute Deviation (MAD)630.98904
Skewness-0.018179346
Sum26507977
Variance906028.08
MonotonicityNot monotonic
2023-07-17T14:36:37.556690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4124.607584 1
 
< 0.1%
5703.215772 1
 
< 0.1%
4812.882434 1
 
< 0.1%
5333.045901 1
 
< 0.1%
4222.062581 1
 
< 0.1%
4867.236591 1
 
< 0.1%
5320.409901 1
 
< 0.1%
5145.525554 1
 
< 0.1%
5691.261186 1
 
< 0.1%
6676.898525 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
2980.732822 1
< 0.1%
3001.256324 1
< 0.1%
3008.225296 1
< 0.1%
3008.670417 1
< 0.1%
3009.358939 1
< 0.1%
3009.63354 1
< 0.1%
3015.243241 1
< 0.1%
3015.625102 1
< 0.1%
3016.462018 1
< 0.1%
3018.267217 1
< 0.1%
ValueCountFrequency (%)
6894.817654 1
< 0.1%
6889.977775 1
< 0.1%
6875.675556 1
< 0.1%
6863.703649 1
< 0.1%
6863.412256 1
< 0.1%
6861.042668 1
< 0.1%
6858.519182 1
< 0.1%
6858.283916 1
< 0.1%
6856.599838 1
< 0.1%
6849.165772 1
< 0.1%

bank_branch_count_8w
Real number (ℝ)

Distinct720
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.98545
Minimum0
Maximum2224
Zeros1021
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:37.683760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q318
95-th percentile1439.05
Maximum2224
Range2224
Interquartile range (IQR)17

Descriptive statistics

Standard deviation448.77222
Coefficient of variation (CV)2.703684
Kurtosis7.8226371
Mean165.98545
Median Absolute Deviation (MAD)7
Skewness2.9804468
Sum912920
Variance201396.5
MonotonicityNot monotonic
2023-07-17T14:36:37.799003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1021
18.6%
1 995
18.1%
2 344
 
6.3%
11 188
 
3.4%
12 154
 
2.8%
10 153
 
2.8%
8 153
 
2.8%
14 146
 
2.7%
9 142
 
2.6%
13 141
 
2.6%
Other values (710) 2063
37.5%
ValueCountFrequency (%)
0 1021
18.6%
1 995
18.1%
2 344
 
6.3%
3 62
 
1.1%
4 65
 
1.2%
5 84
 
1.5%
6 99
 
1.8%
7 97
 
1.8%
8 153
 
2.8%
9 142
 
2.6%
ValueCountFrequency (%)
2224 1
< 0.1%
2206 1
< 0.1%
2192 1
< 0.1%
2179 1
< 0.1%
2168 1
< 0.1%
2152 1
< 0.1%
2143 1
< 0.1%
2136 1
< 0.1%
2135 1
< 0.1%
2127 1
< 0.1%
Distinct34
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5603636
Minimum0
Maximum36
Zeros18
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:37.935099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q312
95-th percentile18
Maximum36
Range36
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0607324
Coefficient of variation (CV)0.59118195
Kurtosis0.74866575
Mean8.5603636
Median Absolute Deviation (MAD)3
Skewness0.81211107
Sum47082
Variance25.611013
MonotonicityNot monotonic
2023-07-17T14:36:38.038926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5 475
 
8.6%
7 469
 
8.5%
6 425
 
7.7%
8 416
 
7.6%
4 379
 
6.9%
9 370
 
6.7%
2 346
 
6.3%
3 344
 
6.3%
11 331
 
6.0%
10 318
 
5.8%
Other values (24) 1627
29.6%
ValueCountFrequency (%)
0 18
 
0.3%
1 180
 
3.3%
2 346
6.3%
3 344
6.3%
4 379
6.9%
5 475
8.6%
6 425
7.7%
7 469
8.5%
8 416
7.6%
9 370
6.7%
ValueCountFrequency (%)
36 2
 
< 0.1%
33 1
 
< 0.1%
32 2
 
< 0.1%
31 1
 
< 0.1%
29 2
 
< 0.1%
28 5
0.1%
27 3
 
0.1%
26 4
 
0.1%
25 10
0.2%
24 12
0.2%

employment_status
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48690909
Minimum0
Maximum6
Zeros4220
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:38.136088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1130055
Coefficient of variation (CV)2.2858588
Kurtosis7.2533784
Mean0.48690909
Median Absolute Deviation (MAD)0
Skewness2.7423494
Sum2678
Variance1.2387811
MonotonicityNot monotonic
2023-07-17T14:36:38.218309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4220
76.7%
1 630
 
11.5%
2 308
 
5.6%
5 161
 
2.9%
3 101
 
1.8%
4 78
 
1.4%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 4220
76.7%
1 630
 
11.5%
2 308
 
5.6%
3 101
 
1.8%
4 78
 
1.4%
5 161
 
2.9%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 161
 
2.9%
4 78
 
1.4%
3 101
 
1.8%
2 308
 
5.6%
1 630
 
11.5%
0 4220
76.7%

credit_risk_score
Real number (ℝ)

Distinct403
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.288
Minimum-131
Maximum376
Zeros2
Zeros (%)< 0.1%
Negative55
Negative (%)1.0%
Memory size85.9 KiB
2023-07-17T14:36:38.324317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-131
5-th percentile34
Q196
median148
Q3209
95-th percentile294
Maximum376
Range507
Interquartile range (IQR)113

Descriptive statistics

Standard deviation78.947403
Coefficient of variation (CV)0.51168855
Kurtosis-0.45474767
Mean154.288
Median Absolute Deviation (MAD)57
Skewness0.18614151
Sum848584
Variance6232.6925
MonotonicityNot monotonic
2023-07-17T14:36:38.444867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 42
 
0.8%
86 39
 
0.7%
172 36
 
0.7%
120 36
 
0.7%
108 35
 
0.6%
100 34
 
0.6%
101 34
 
0.6%
143 33
 
0.6%
99 33
 
0.6%
163 32
 
0.6%
Other values (393) 5146
93.6%
ValueCountFrequency (%)
-131 1
< 0.1%
-102 1
< 0.1%
-97 1
< 0.1%
-88 1
< 0.1%
-87 1
< 0.1%
-81 1
< 0.1%
-80 1
< 0.1%
-78 1
< 0.1%
-75 1
< 0.1%
-73 1
< 0.1%
ValueCountFrequency (%)
376 1
< 0.1%
375 1
< 0.1%
374 1
< 0.1%
365 1
< 0.1%
360 1
< 0.1%
356 1
< 0.1%
355 1
< 0.1%
354 1
< 0.1%
353 1
< 0.1%
351 2
< 0.1%

email_is_free
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
1
3318 
0
2182 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 3318
60.3%
0 2182
39.7%

Length

2023-07-17T14:36:38.550445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:38.640238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3318
60.3%
0 2182
39.7%

Most occurring characters

ValueCountFrequency (%)
1 3318
60.3%
0 2182
39.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3318
60.3%
0 2182
39.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3318
60.3%
0 2182
39.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3318
60.3%
0 2182
39.7%

housing_status
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3470909
Minimum0
Maximum5
Zeros1957
Zeros (%)35.6%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:38.704963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3056596
Coefficient of variation (CV)0.96924385
Kurtosis-0.41167567
Mean1.3470909
Median Absolute Deviation (MAD)1
Skewness0.70759913
Sum7409
Variance1.7047469
MonotonicityNot monotonic
2023-07-17T14:36:38.785577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1957
35.6%
2 1649
30.0%
1 1114
20.3%
4 631
 
11.5%
3 136
 
2.5%
5 13
 
0.2%
ValueCountFrequency (%)
0 1957
35.6%
1 1114
20.3%
2 1649
30.0%
3 136
 
2.5%
4 631
 
11.5%
5 13
 
0.2%
ValueCountFrequency (%)
5 13
 
0.2%
4 631
 
11.5%
3 136
 
2.5%
2 1649
30.0%
1 1114
20.3%
0 1957
35.6%

phone_home_valid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
3701 
1
1799 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 3701
67.3%
1 1799
32.7%

Length

2023-07-17T14:36:38.873642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:38.967579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3701
67.3%
1 1799
32.7%

Most occurring characters

ValueCountFrequency (%)
0 3701
67.3%
1 1799
32.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3701
67.3%
1 1799
32.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3701
67.3%
1 1799
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3701
67.3%
1 1799
32.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
1
4841 
0
659 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 4841
88.0%
0 659
 
12.0%

Length

2023-07-17T14:36:39.045678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:39.141152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4841
88.0%
0 659
 
12.0%

Most occurring characters

ValueCountFrequency (%)
1 4841
88.0%
0 659
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4841
88.0%
0 659
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4841
88.0%
0 659
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4841
88.0%
0 659
 
12.0%

bank_months_count
Real number (ℝ)

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.844182
Minimum-1
Maximum31
Zeros0
Zeros (%)0.0%
Negative1680
Negative (%)30.5%
Memory size85.9 KiB
2023-07-17T14:36:39.220738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median4
Q325
95-th percentile30
Maximum31
Range32
Interquartile range (IQR)26

Descriptive statistics

Standard deviation12.49353
Coefficient of variation (CV)1.1520952
Kurtosis-1.4805654
Mean10.844182
Median Absolute Deviation (MAD)5
Skewness0.4848086
Sum59643
Variance156.08828
MonotonicityNot monotonic
2023-07-17T14:36:39.327619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
-1 1680
30.5%
1 860
15.6%
30 428
 
7.8%
28 394
 
7.2%
15 277
 
5.0%
31 263
 
4.8%
25 236
 
4.3%
10 200
 
3.6%
20 174
 
3.2%
2 163
 
3.0%
Other values (19) 825
15.0%
ValueCountFrequency (%)
-1 1680
30.5%
1 860
15.6%
2 163
 
3.0%
3 24
 
0.4%
4 25
 
0.5%
5 152
 
2.8%
6 67
 
1.2%
7 5
 
0.1%
9 28
 
0.5%
10 200
 
3.6%
ValueCountFrequency (%)
31 263
4.8%
30 428
7.8%
29 47
 
0.9%
28 394
7.2%
27 36
 
0.7%
26 97
 
1.8%
25 236
4.3%
24 16
 
0.3%
23 4
 
0.1%
22 20
 
0.4%

has_other_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
4629 
1
871 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4629
84.2%
1 871
 
15.8%

Length

2023-07-17T14:36:39.438391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:39.532162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4629
84.2%
1 871
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 4629
84.2%
1 871
 
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4629
84.2%
1 871
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4629
84.2%
1 871
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4629
84.2%
1 871
 
15.8%

proposed_credit_limit
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean665.57455
Minimum200
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:39.602818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile200
Q1200
median200
Q31500
95-th percentile1500
Maximum2100
Range1900
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation586.87529
Coefficient of variation (CV)0.88175741
Kurtosis-0.86720084
Mean665.57455
Median Absolute Deviation (MAD)0
Skewness0.83503247
Sum3660660
Variance344422.6
MonotonicityNot monotonic
2023-07-17T14:36:39.688694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
200 2815
51.2%
1500 1179
21.4%
500 759
 
13.8%
1000 448
 
8.1%
2000 200
 
3.6%
990 52
 
0.9%
510 22
 
0.4%
1900 18
 
0.3%
490 5
 
0.1%
210 1
 
< 0.1%
ValueCountFrequency (%)
200 2815
51.2%
210 1
 
< 0.1%
490 5
 
0.1%
500 759
 
13.8%
510 22
 
0.4%
990 52
 
0.9%
1000 448
 
8.1%
1500 1179
21.4%
1900 18
 
0.3%
2000 200
 
3.6%
ValueCountFrequency (%)
2100 1
 
< 0.1%
2000 200
 
3.6%
1900 18
 
0.3%
1500 1179
21.4%
1000 448
 
8.1%
990 52
 
0.9%
510 22
 
0.4%
500 759
13.8%
490 5
 
0.1%
210 1
 
< 0.1%

foreign_request
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
5303 
1
 
197

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5303
96.4%
1 197
 
3.6%

Length

2023-07-17T14:36:39.786197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:39.893974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5303
96.4%
1 197
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 5303
96.4%
1 197
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5303
96.4%
1 197
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5303
96.4%
1 197
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5303
96.4%
1 197
 
3.6%

source
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
5461 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5461
99.3%
1 39
 
0.7%

Length

2023-07-17T14:36:39.969800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:40.070970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5461
99.3%
1 39
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 5461
99.3%
1 39
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5461
99.3%
1 39
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5461
99.3%
1 39
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5461
99.3%
1 39
 
0.7%

session_length_in_minutes
Real number (ℝ)

Distinct5492
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9056275
Minimum-1
Maximum77.730242
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)0.1%
Memory size85.9 KiB
2023-07-17T14:36:40.179415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.292612
Q13.1836955
median5.1121734
Q38.8312064
95-th percentile24.807428
Maximum77.730242
Range78.730242
Interquartile range (IQR)5.6475109

Descriptive statistics

Standard deviation8.9029198
Coefficient of variation (CV)1.1261497
Kurtosis13.060556
Mean7.9056275
Median Absolute Deviation (MAD)2.4862117
Skewness3.2216353
Sum43480.951
Variance79.261981
MonotonicityNot monotonic
2023-07-17T14:36:40.328956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 8
 
0.1%
6.022198272 2
 
< 0.1%
4.456483055 1
 
< 0.1%
8.907309959 1
 
< 0.1%
4.352656473 1
 
< 0.1%
4.789083314 1
 
< 0.1%
1.732422481 1
 
< 0.1%
0.445080736 1
 
< 0.1%
7.866015689 1
 
< 0.1%
5.840825551 1
 
< 0.1%
Other values (5482) 5482
99.7%
ValueCountFrequency (%)
-1 8
0.1%
0.06597924678 1
 
< 0.1%
0.1110217821 1
 
< 0.1%
0.1449846511 1
 
< 0.1%
0.1647204077 1
 
< 0.1%
0.2360232136 1
 
< 0.1%
0.265162398 1
 
< 0.1%
0.3099296667 1
 
< 0.1%
0.3379993126 1
 
< 0.1%
0.3772451809 1
 
< 0.1%
ValueCountFrequency (%)
77.73024155 1
< 0.1%
73.67424181 1
< 0.1%
69.90564674 1
< 0.1%
69.45262167 1
< 0.1%
67.60214552 1
< 0.1%
65.16586886 1
< 0.1%
64.61179558 1
< 0.1%
64.57177683 1
< 0.1%
64.15290397 1
< 0.1%
63.82582581 1
< 0.1%

device_os
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
3
2341 
2
1420 
0
1363 
1
326 
4
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 2341
42.6%
2 1420
25.8%
0 1363
24.8%
1 326
 
5.9%
4 50
 
0.9%

Length

2023-07-17T14:36:40.460680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:40.568008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 2341
42.6%
2 1420
25.8%
0 1363
24.8%
1 326
 
5.9%
4 50
 
0.9%

Most occurring characters

ValueCountFrequency (%)
3 2341
42.6%
2 1420
25.8%
0 1363
24.8%
1 326
 
5.9%
4 50
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2341
42.6%
2 1420
25.8%
0 1363
24.8%
1 326
 
5.9%
4 50
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2341
42.6%
2 1420
25.8%
0 1363
24.8%
1 326
 
5.9%
4 50
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2341
42.6%
2 1420
25.8%
0 1363
24.8%
1 326
 
5.9%
4 50
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
2988 
1
2512 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2988
54.3%
1 2512
45.7%

Length

2023-07-17T14:36:40.670111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:40.761348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2988
54.3%
1 2512
45.7%

Most occurring characters

ValueCountFrequency (%)
0 2988
54.3%
1 2512
45.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2988
54.3%
1 2512
45.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2988
54.3%
1 2512
45.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2988
54.3%
1 2512
45.7%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
1
5111 
2
 
326
0
 
60
-1
 
3

Length

Max length2
Median length1
Mean length1.0005455
Min length1

Characters and Unicode

Total characters5503
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5111
92.9%
2 326
 
5.9%
0 60
 
1.1%
-1 3
 
0.1%

Length

2023-07-17T14:36:40.844510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:40.944752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5114
93.0%
2 326
 
5.9%
0 60
 
1.1%

Most occurring characters

ValueCountFrequency (%)
1 5114
92.9%
2 326
 
5.9%
0 60
 
1.1%
- 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
99.9%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5114
93.0%
2 326
 
5.9%
0 60
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5503
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5114
92.9%
2 326
 
5.9%
0 60
 
1.1%
- 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5503
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5114
92.9%
2 326
 
5.9%
0 60
 
1.1%
- 3
 
0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
5500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5500
100.0%

Length

2023-07-17T14:36:41.027127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:36:41.398997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5500
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5500
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5500
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5500
100.0%

month
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4332727
Minimum0
Maximum7
Zeros734
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:36:41.467551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2709096
Coefficient of variation (CV)0.66144167
Kurtosis-1.2089142
Mean3.4332727
Median Absolute Deviation (MAD)2
Skewness0.021075218
Sum18883
Variance5.1570306
MonotonicityNot monotonic
2023-07-17T14:36:41.549999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 766
13.9%
0 734
13.3%
2 698
12.7%
5 689
12.5%
6 685
12.5%
4 662
12.0%
1 646
11.7%
7 620
11.3%
ValueCountFrequency (%)
0 734
13.3%
1 646
11.7%
2 698
12.7%
3 766
13.9%
4 662
12.0%
5 689
12.5%
6 685
12.5%
7 620
11.3%
ValueCountFrequency (%)
7 620
11.3%
6 685
12.5%
5 689
12.5%
4 662
12.0%
3 766
13.9%
2 698
12.7%
1 646
11.7%
0 734
13.3%

Interactions

2023-07-17T14:36:30.906604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:50.024973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.111535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:54.063124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:56.432231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:58.457692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:00.446520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.757538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:04.903422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.954686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.332200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:11.438268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:13.827040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.837384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.957215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.265844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:22.273684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:24.313458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:26.670132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.734234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:31.049960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:50.161460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.209014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:54.163078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:56.537308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:58.554832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:00.550530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.876018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.013959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:07.080748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.446007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:11.546324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:13.948092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.942366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:18.082519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.370719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:22.396794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:24.424846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:26.793376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.840473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:31.175955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:50.271823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.302148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:54.264013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:56.630360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:58.647274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:00.642435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.986377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.104903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:07.174867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.542064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:11.656321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.060122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:16.036890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:18.196320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.465171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:22.506450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:24.516918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:26.904545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.951608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:31.308642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:50.376370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.398558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:54.381594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:56.727196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:58.745434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:00.743207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:03.085353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.204025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:07.566740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.644656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:11.758690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.170993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:16.140419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:18.295529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.559186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:22.618554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:24.615581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.010385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.055730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:31.445007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:50.483032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.503767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:54.510701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:56.829540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:58.846745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:00.844538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:03.183921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.314547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:07.668652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.749129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:11.866126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.269322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:16.247855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:18.406192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.658679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:22.722295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:24.714757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.119613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.168288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:31.577987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:50.603421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.597754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:54.612419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:56.928431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:58.966323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:01.224001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:03.283144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.420908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:07.771827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.851480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:11.973796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.373005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:16.365965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:18.505963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.761504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:22.828449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:24.814309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.221571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.270689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:31.710064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:50.712634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.706115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:54.711045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.025285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.061094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:01.355948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:03.390327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.519350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:07.894703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.950020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.094837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.475385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:16.493304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:18.606858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.871716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:22.945039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:24.911318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.323728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.378555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:31.827882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:50.805446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.811927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:54.805302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.118894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.156039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:01.447549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:03.502488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.624733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:07.988442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.043228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.186373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.563236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:16.595218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:18.707126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.959026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.031386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:25.018720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.417214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.485982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:32.236576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:50.900478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.908354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:54.906185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.213183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.249363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:01.559315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:03.610471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.724377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:08.083711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.140176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.281492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.654487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:16.693447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:18.800381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.053050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.121967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:25.117876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.512769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.583687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:32.362749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.004896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.003992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:55.027179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.319046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.356826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:01.667302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:03.722419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.828934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:08.187367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.244051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.390378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.761214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:16.798336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:18.902476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.150606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.222605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:25.222015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.614373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.689344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:32.470221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.106545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.100328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:55.127441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.419032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.458227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:01.765470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:03.819322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:05.926666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:08.309700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.347674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.493631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.859312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:16.902592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:19.000049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.255138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.320693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:25.340768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.712905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.796090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:32.576350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.207856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.213557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:55.494022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.523784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.559646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:01.866059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:03.926928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.029732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:08.431316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.470203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.605772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:14.957675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.008117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:19.107671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.384702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.422292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:25.459584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.815967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.902075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:32.668721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.295047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.307393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:55.606973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.611565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.647186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:01.953225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:04.027280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.118463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:08.525043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.561578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.705481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.041914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.114476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:19.195846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.484921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.508063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:25.548647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:27.905717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:29.992905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:32.786065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.404454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.406667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:55.703626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.711996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.745675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.058393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:04.137174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.221461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:08.642754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.663064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.805992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.138365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.216934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:19.298353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.582560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.606792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:25.943164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.006243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:30.102220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:32.889607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.510605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.501124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:55.796219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.808455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.839674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.168478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:04.243391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.321407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:08.739767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.770533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.902154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.235112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.327630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:19.704381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.676721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.697819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:26.034935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.105619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:30.200614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:32.987661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.617506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.585453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:55.886696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.901789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:59.931291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.261482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:04.358431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.433121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:08.833560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.876539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:12.997229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.344000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.437820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:19.792671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.763982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.806245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:26.139418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.218473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:30.303874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:33.081393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.717725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.684499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:55.980708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:57.997374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:00.021864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.357148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:04.471508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.526641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:08.927772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:10.982976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:13.386892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.445427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.538899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:19.879462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.860695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.894542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:26.239736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.320637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:30.407743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:33.181780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.812599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.774830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:56.092874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:58.094981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:00.118783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.456641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:04.582084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.637479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.024712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:11.090237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:13.486068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.550581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.651723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:19.971605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:21.963834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:23.990291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:26.336988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.419150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:30.508152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:33.287029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:51.913085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.871818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:56.200706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:58.216280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:00.218694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.554770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:04.682664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.747030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.126257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:11.194093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:13.588078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.644642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.753720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.065628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:22.061093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:24.086153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:26.441734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.519810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:30.617558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:33.408314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:52.012984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:53.967476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:56.315768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:58.346370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:00.338263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:02.654540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:04.790178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:06.854249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:09.227885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:11.322654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:13.709110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:15.744126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:17.854684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:20.167225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:22.178485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:24.196764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:26.547727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:28.623428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:36:30.765922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2023-07-17T14:36:33.589421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-17T14:36:33.979618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fraud_boolincomename_email_similarityprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wdate_of_birth_distinct_emails_4wemployment_statuscredit_risk_scoreemail_is_freehousing_statusphone_home_validphone_mobile_validbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_distinct_emails_8wdevice_fraud_countmonth
941400.80.779245-1138300.021109-1.25377237451312.0453212941.5568814124.60758410110134111120200.0004.82885101105
569510.90.331740-1171300.0186000.00180331838733.2875956538.8303654234.77812410202651001101500.0002.33975330101
561200.70.993781273203.168827-0.939387215197589.8561336937.2828245961.40443319311321201-10500.0005.66665830100
260500.80.675565-140400.019582-1.38273618596014.0817703388.1318344326.567101107080011110200.00010.23621720206
137110.90.711604-188200.027377-1.09370306064315.0407964234.7361333008.670417149090141010200.0001.00818221107
707100.40.370633536300.001329-0.688428114283111.5497126120.4460975046.207629129712018014012001000.0005.58164721103
956300.50.272059101200.029865-1.045670215646011.5230284990.3731955066.18911611001291201-10500.0006.75943431107
108900.80.8183981204400.017229-1.60618235836011.4728805069.3465785551.66716415140157110110200.00011.41641431102
268910.90.057792-1133600.011751-0.487804146274061.4501934847.5355754870.42304615122601010250500.00023.61379730103
866710.90.150778-1150509.19502848.672171014604926.1612906058.0998736611.081328330501301280200.0002.39136131101
fraud_boolincomename_email_similarityprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wdate_of_birth_distinct_emails_4wemployment_statuscredit_risk_scoreemail_is_freehousing_statusphone_home_validphone_mobile_validbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_distinct_emails_8wdevice_fraud_countmonth
451800.10.340333540206.196026-1.003855233699729.5082598344.0421815310.4434573011038120110200.00044.91807801102
318600.90.994330-150300.00015048.01201207875011.3941674892.7422995601.34022081201261101161200.0006.07437100102
75710.50.125351-1194500.041308-0.74076817211620.3968903738.4354533094.142196645002511001501500.00020.63784800100
873400.50.988162-178400.016588-1.60020813952519.6743834874.5384454682.53742211751930111101000.0001.39640320104
283600.10.809111-1206200.008169-0.737300337827427.8355267127.3425106805.0504291121109040110200.0008.60405731100
547100.10.344062-135200.002665-1.568294126052502.9516515447.0464374823.8933052124961201210200.0006.33489521104
916010.10.090230-195800.004980-1.64979118893734.1029424368.2797084228.5615160222261011-101500.0004.22187530105
48010.40.469968-150303.032884-0.929847239824684.5442865187.8712864285.0649371130750401-10200.0012.34280730106
173310.80.151689-149503.17916450.03426706432475.8864323147.0783123972.770811180990001-10200.0007.20084820106
823900.40.106095273300.027857-0.671924115655197.4823645864.9836774693.45273681911091201200500.0005.28628201103